Event generation in response to network intent formal equivalence failures

ABSTRACT

Systems, methods, and computer-readable media for receiving an indication of an equivalence failure, the equivalence failure corresponding to one or more models of network intents. The indication of the equivalence failure is analyzed and one or more constituent intents that caused the equivalence failure are identified, wherein the one or more constituent intents are associated with a model of the one or more models of network intents. The granularity of the equivalence failure and the identified one or more constituent intents is determined, and an event for external consumption is generated, the event based at least in part on the equivalence failure, the granularity of the equivalence failure, and the identified one or more constituent intents.

CROSS-REFERENCE TO RELATED APPLICATIONS

The instant application is a Continuation of, and claims priority to,U.S. patent application Ser. No. 16/752,198, entitled EVENT GENERATIONIN RESPONSE TO NETWORK INTENT FORMAL EQUIVALENCE FAILURES filed Jan. 24,2020, which is a Continuation of, and claims priority to, U.S. patentapplication Ser. No. 15/662,906, entitled EVENT GENERATION IN RESPONSETO NETWORK INTENT FORMAL EQUIVALENCE FAILURES filed Jul. 28, 2017, whichclaims the benefit of, and priority to, U.S. Provisional PatentApplication No. 62/520,722, entitled EVENT GENERATION IN RESPONSE TONETWORK INTENT FORMAL EQUIVALENCE FAILURES, filed on Jun. 16, 2017, thecontents of which are hereby expressly incorporated by reference intheir entireties.

TECHNICAL FIELD

The present technology pertains to network configuration andtroubleshooting, and more specifically to assurance of networkconfigurations via external events for formal equivalence failures.

BACKGROUND

Computer networks are becoming increasingly complex, often involving lowlevel as well as high level configurations at various layers of thenetwork. For example, computer networks generally include numerousaccess policies, forwarding policies, routing policies, securitypolicies, quality-of-service (QoS) policies, etc., which together definethe overall behavior and operation of the network. Network operatorshave a wide array of configuration options for tailoring the network tothe needs of the users. While the different configuration optionsavailable provide network operators a great degree of flexibility andcontrol over the network, they also add to the complexity of thenetwork. In many cases, the configuration process can become highlycomplex. Not surprisingly, the network configuration process isincreasingly error prone. In addition, troubleshooting errors in ahighly complex network can be extremely difficult. The process ofidentifying the root cause of undesired behavior in the network can be adaunting task.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only exemplary embodiments of the disclosure and are nottherefore to be considered to be limiting of its scope, the principlesherein are described and explained with additional specificity anddetail through the use of the accompanying drawings in which:

FIGS. 1A and 1B illustrate example network environments;

FIG. 2A illustrates an example object model for a network;

FIG. 2B illustrates an example object model for a tenant object in theexample object model from FIG. 2A;

FIG. 2C illustrates an example association of various objects in theexample object model from FIG. 2A;

FIG. 2D illustrates a schematic diagram of example models forimplementing the example object model from FIG. 2A;

FIG. 3A illustrates an example network assurance appliance;

FIG. 3B illustrates an example system for network assurance;

FIG. 3C illustrates a schematic diagram of an example system for staticpolicy analysis in a network;

FIG. 4 illustrates an example method embodiment for network assuranceand fault code aggregation;

FIG. 5A illustrates an example architecture for formal analysis;

FIG. 5B illustrates a chart of events triggered by paired outcomes ofmodel evaluations;

FIG. 6 illustrates an example network device in accordance with variousembodiments; and

FIG. 7 illustrates an example computing device in accordance withvarious embodiments.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Various embodiments of the disclosure are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the disclosure.Thus, the following description and drawings are illustrative and arenot to be construed as limiting. Numerous specific details are describedto provide a thorough understanding of the disclosure. However, incertain instances, well-known or conventional details are not describedin order to avoid obscuring the description. References to one or anembodiment in the present disclosure can be references to the sameembodiment or any embodiment; and, such references mean at least one ofthe embodiments.

Reference to “one embodiment” or “an embodiment” means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment,nor are separate or alternative embodiments mutually exclusive of otherembodiments. Moreover, various features are described which may beexhibited by some embodiments and not by others.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the disclosure, and in thespecific context where each term is used. Alternative language andsynonyms may be used for any one or more of the terms discussed herein,and no special significance should be placed upon whether or not a termis elaborated or discussed herein. In some cases, synonyms for certainterms are provided. A recital of one or more synonyms does not excludethe use of other synonyms. The use of examples anywhere in thisspecification including examples of any terms discussed herein isillustrative only, and is not intended to further limit the scope andmeaning of the disclosure or of any example term. Likewise, thedisclosure is not limited to various embodiments given in thisspecification.

Without intent to limit the scope of the disclosure, examples ofinstruments, apparatus, methods and their related results according tothe embodiments of the present disclosure are given below. Note thattitles or subtitles may be used in the examples for convenience of areader, which in no way should limit the scope of the disclosure. Unlessotherwise defined, technical and scientific terms used herein have themeaning as commonly understood by one of ordinary skill in the art towhich this disclosure pertains. In the case of conflict, the presentdocument, including definitions will control.

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

Overview

Disclosed herein are systems, methods, and computer-readable media forgenerating events for external consumption, wherein the events are basedon various failures of equivalence checks between two or more models ofnetwork intents. In some examples, a system can obtain an indication ofa network equivalence failure (e.g. a software-defined logical intent isnot rendered correctly into a switch as a corresponding hardwareintent), and subsequently analyze the indication of the networkequivalence failure in conjunction with the associated models of userintents (e.g. the software model and hardware model in the exampleabove) to generate an event for external consumption.

The logical model of network intents can be a model generated based onconfigurations defined in one or more controllers or servers in asoftware-defined network (SDN), such as an APIC (application policyinfrastructure controller) in an ACI (application-centricinfrastructure) network. The logical model can thus represent thelogical configuration of the SDN network (e.g., a representation of thelogical configurations in the ACI). The logical configuration of the SDNnetwork can be based on the configurations defined by the networkoperator for the SDN network, such as the configurations entered intothe APIC of an ACI network, and may thus reflect the intent of thenetwork operator or the intended behavior of the SDN network.

The hardware model of network intents can be a model generated based onthe logical model. The hardware model can thus represent the hardwarerendering of the discrete software-defined components that comprise thelogical model. Often times, there is not a one-to-one correspondencebetween a software-defined logical intent and a hardware-defined intent.For example, the hardware rendering of the logical model might cause asingle logical intent to be broken into multiple different hardwareintents. This is not problematic in and of itself, as long as themultiple hardware intents capture the exact same effect as the singlelogical intent. However, conventional network assurance processesstruggle to make this determination, as it requires a comparison of twomodels of network intents that do not have a congruent form. As such, itwould be desirable to provide intelligent network assurance via eventgeneration that is responsive to equivalence failures between two ormore models of network intents that are not necessarily congruent inform or composition.

DESCRIPTION

The disclosed technology addresses the need in the art for a reliableand efficient ability to generate events corresponding to conflict rulesor other equivalence failures between two or more models of networkintents. The present technology involves systems, methods, andcomputer-readable media for receiving as input one or more equivalencefailures and subsequently generating one or more events corresponding tothe input equivalence failures. For example, the input equivalencefailures might be the set of conflict rules calculated between two ormore models of network intents, in which case one or more events wouldbe generated corresponding to the conflict rules between the models ofnetwork intents. The present technology will be described in thefollowing disclosure as follows. The discussion begins with anintroductory discussion of network assurance and a description ofexample computing environments, as illustrated in FIGS. 1A and 1B. Thediscussion continues with a description of systems and methods fornetwork assurance, network modeling, and event generation, as shown inFIGS. 2A-2D, 3A-C, and 4. The discussion moves next to an example formalanalysis architecture for event generation as illustrated in FIG. 5A anda chart of events triggered by paired outcomes of model comparisons asseen in FIG. 5B. The discussion then concludes with a description of anexample network device, as illustrated in FIG. 6 , and an examplecomputing device, as illustrated in FIG. 7 , including example hardwarecomponents suitable for hosting software applications and performingcomputing operations. The disclosure now turns to a discussion ofnetwork assurance, the analysis and execution of which is a precursor tothe event generation in accordance with embodiments of the presentdisclosure.

Network assurance is the guarantee or determination that the network isbehaving as intended by the network operator and has been configuredproperly (e.g., the network is doing what it is intended to do). Intentcan encompass various network operations, such as bridging, routing,security, service chaining, endpoints, compliance, QoS (Quality ofService), audits, etc. Intent can be embodied in one or more policies,settings, configurations, etc., defined for the network and individualnetwork elements (e.g., switches, routers, applications, resources,etc.). However, often times, the configurations, policies, etc., definedby a network operator are incorrect or not accurately reflected in theactual behavior of the network. For example, a network operatorspecifies a configuration A for one or more types of traffic but laterfinds out that the network is actually applying configuration B to thattraffic or otherwise processing that traffic in a manner that isinconsistent with configuration A. This can be a result of manydifferent causes, such as hardware errors, software bugs, varyingpriorities, configuration conflicts, misconfiguration of one or moresettings, improper rule rendering by devices, unexpected errors orevents, software upgrades, configuration changes, failures, etc. Asanother example, a network operator implements configuration C but oneor more other configurations result in the network behaving in a mannerthat is inconsistent with the intent reflected by the implementation ofconfiguration C. For example, such a situation can result whenconfiguration C conflicts with other configurations in the network.

The approaches herein can provide network assurance by modeling variousaspects of the network and/or performing consistency checks as well asother network assurance checks. The network assurance approaches hereincan be implemented in various types of networks, including a privatenetwork, such as a local area network (LAN); an enterprise network; astandalone or traditional network, such as a data center network; anetwork including a physical or underlay layer and a logical or overlaylayer, such as a VXLAN or software-defined network (SDN) (e.g.,Application Centric Infrastructure (ACI) or VMware NSX networks); etc.The approaches herein can also enable identification and visualizationof hardware-level (e.g., network switch-level) errors along any softwareor application-centric dimension. In this manner, data center operatorscan quickly see hardware errors that impact particular tenants or otherlogical entities, across the entire network fabric, and even drill downby other dimensions, such as endpoint groups, to see only those relevanthardware errors. These visualizations speed root cause analysis,improving data center and application availability metrics.

Logical models can be implemented to represent various aspects of anetwork. A model can include a mathematical or semantic model of thenetwork, including, without limitation the network's policies,configurations, requirements, security, routing, topology, applications,hardware, filters, contracts, access control lists, EPGs, applicationprofiles, tenants, etc. Models can be implemented to provide networkassurance to ensure that the network is properly configured and thebehavior of the network will be consistent (or is consistent) with theintended behavior reflected through specific policies, settings,definitions, etc., implemented by the network operator. Unliketraditional network monitoring which involves sending and analyzing datapackets and observing network behavior, network assurance can beperformed through modeling without necessarily ingesting any packet dataor monitoring traffic or network behavior. This can result in foresight,insight, and hindsight: problems can be prevented before they occur,identified when they occur, and fixed immediately after they occur.

Properties of the network can be mathematically modeled todeterministically predict the behavior and condition of the network. Amathematical model can abstract the control, management, and dataplanes, and may use various techniques such as symbolic, formalverification, consistency, graph, behavioral, etc. The network can bedetermined to be healthy if the model(s) indicate proper behavior (e.g.,no inconsistencies, conflicts, errors, etc.). The network can bedetermined to be functional, but not fully healthy, if the modelingindicates proper behavior but some inconsistencies. The network can bedetermined to be non-functional and not healthy if the modelingindicates improper behavior and errors. If inconsistencies or errors aredetected by the modeling, a detailed analysis of the correspondingmodel(s) can allow one or more underlying or root problems to beidentified with great accuracy.

The models can consume numerous types of data and/or events which modela large amount of behavioral aspects of the network. Such data andevents can impact various aspects of the network, such as underlayservices, overlay service, tenant connectivity, tenant security, tenantEP mobility, tenant policy, resources, etc.

Having described various aspects of network assurance and fault codeaggregation across dimensions, the disclosure now turns to a discussionof example network environments for network assurance.

FIG. 1A illustrates a diagram of an example Network Environment 100,such as a data center. The Network Environment 100 can include a Fabric120 which can represent the physical layer or infrastructure (e.g.,underlay) of the Network Environment 100. Fabric 120 can include Spines102 (e.g., spine routers or switches) and Leafs 104 (e.g., leaf routersor switches) which can be interconnected for routing or switchingtraffic in the Fabric 120. Spines 102 can interconnect Leafs 104 in theFabric 120, and Leafs 104 can connect the Fabric 120 to an overlay orlogical portion of the Network Environment 100, which can includeapplication services, servers, virtual machines, containers, endpoints,etc. Thus, network connectivity in the Fabric 120 can flow from Spines102 to Leafs 104, and vice versa. The interconnections between Leafs 104and Spines 102 can be redundant (e.g., multiple interconnections) toavoid a failure in routing. In some embodiments, Leafs 104 and Spines102 can be fully connected, such that any given Leaf is connected toeach of the Spines 102, and any given Spine is connected to each of theLeafs 104. Leafs 104 can be, for example, top-of-rack (“ToR”) switches,aggregation switches, gateways, ingress and/or egress switches, provideredge devices, and/or any other type of routing or switching device.

Leafs 104 can be responsible for routing and/or bridging tenant orcustomer packets and applying network policies or rules. Networkpolicies and rules can be driven by one or more Controllers 116, and/orimplemented or enforced by one or more devices, such as Leafs 104. Leafs104 can connect other elements to the Fabric 120. For example, Leafs 104can connect Servers 106, Hypervisors 108, Virtual Machines (VMs) 110,Applications 112, Network Device 114, etc., with Fabric 120. Suchelements can reside in one or more logical or virtual layers ornetworks, such as an overlay network. In some cases, Leafs 104 canencapsulate and decapsulate packets to and from such elements (e.g.,Servers 106) in order to enable communications throughout NetworkEnvironment 100 and Fabric 120. Leafs 104 can also provide any otherdevices, services, tenants, or workloads with access to Fabric 120. Insome cases, Servers 106 connected to Leafs 104 can similarly encapsulateand decapsulate packets to and from Leafs 104. For example, Servers 106can include one or more virtual switches or routers or tunnel endpointsfor tunneling packets between an overlay or logical layer hosted by, orconnected to, Servers 106 and an underlay layer represented by Fabric120 and accessed via Leafs 104.

Applications 112 can include software applications, services,containers, appliances, functions, service chains, etc. For example,Applications 112 can include a firewall, a database, a CDN server, anIDS/IPS, a deep packet inspection service, a message router, a virtualswitch, etc. An application from Applications 112 can be distributed,chained, or hosted by multiple endpoints (e.g., Servers 106, VMs 110,etc.), or may run or execute entirely from a single endpoint.

VMs 110 can be virtual machines hosted by Hypervisors 108 or virtualmachine managers running on Servers 106. VMs 110 can include workloadsrunning on a guest operating system on a respective server. Hypervisors108 can provide a layer of software, firmware, and/or hardware thatcreates, manages, and/or runs the VMs 110. Hypervisors 108 can allow VMs110 to share hardware resources on Servers 106, and the hardwareresources on Servers 106 to appear as multiple, separate hardwareplatforms. Moreover, Hypervisors 108 on Servers 106 can host one or moreVMs 110.

In some cases, VMs 110 and/or Hypervisors 108 can be migrated to otherServers 106. Servers 106 can similarly be migrated to other locations inNetwork Environment 100. For example, a server connected to a specificleaf can be changed to connect to a different or additional leaf. Suchconfiguration or deployment changes can involve modifications tosettings, configurations and policies that are applied to the resourcesbeing migrated as well as other network components.

In some cases, one or more Servers 106, Hypervisors 108, and/or VMs 110can represent or reside in a tenant or customer space. Tenant space caninclude workloads, services, applications, devices, networks, and/orresources that are associated with one or more clients or subscribers.Accordingly, traffic in Network Environment 100 can be routed based onspecific tenant policies, spaces, agreements, configurations, etc.Moreover, addressing can vary between one or more tenants. In someconfigurations, tenant spaces can be divided into logical segmentsand/or networks and separated from logical segments and/or networksassociated with other tenants. Addressing, policy, security andconfiguration information between tenants can be managed by Controllers116, Servers 106, Leafs 104, etc.

Configurations in Network Environment 100 can be implemented at alogical level, a hardware level (e.g., physical), and/or both. Forexample, configurations can be implemented at a logical and/or hardwarelevel based on endpoint or resource attributes, such as endpoint typesand/or application groups or profiles, through a software-definednetwork (SDN) framework (e.g., Application-Centric Infrastructure (ACI)or VMWARE NSX). To illustrate, one or more administrators can defineconfigurations at a logical level (e.g., application or software level)through Controllers 116, which can implement or propagate suchconfigurations through Network Environment 100. In some examples,Controllers 116 can be Application Policy Infrastructure Controllers(APICs) in an ACI framework. In other examples, Controllers 116 can beone or more management components for associated with other SDNsolutions, such as NSX Managers.

Such configurations can define rules, policies, priorities, protocols,attributes, objects, etc., for routing and/or classifying traffic inNetwork Environment 100. For example, such configurations can defineattributes and objects for classifying and processing traffic based onEndpoint Groups (EPGs), Security Groups (SGs), VM types, bridge domains(BDs), virtual routing and forwarding instances (VRFs), tenants,priorities, firewall rules, etc. Other example network objects andconfigurations are further described below. Traffic policies and rulescan be enforced based on tags, attributes, or other characteristics ofthe traffic, such as protocols associated with the traffic, EPGsassociated with the traffic, SGs associated with the traffic, networkaddress information associated with the traffic, etc. Such policies andrules can be enforced by one or more elements in Network Environment100, such as Leafs 104, Servers 106, Hypervisors 108, Controllers 116,etc. As previously explained, Network Environment 100 can be configuredaccording to one or more particular software-defined network (SDN)solutions, such as CISCO ACI or VMWARE NSX. These example SDN solutionsare briefly described below.

ACI can provide an application-centric or policy-based solution throughscalable distributed enforcement. ACI supports integration of physicaland virtual environments under a declarative configuration model fornetworks, servers, services, security, requirements, etc. For example,the ACI framework implements EPGs, which can include a collection ofendpoints or applications that share common configuration requirements,such as security, QoS, services, etc. Endpoints can be virtual/logicalor physical devices, such as VMs, containers, hosts, or physical serversthat are connected to Network Environment 100. Endpoints can have one ormore attributes such as a VM name, guest OS name, a security tag,application profile, etc. Application configurations can be appliedbetween EPGs, instead of endpoints directly, in the form of contracts.Leafs 104 can classify incoming traffic into different EPGs. Theclassification can be based on, for example, a network segmentidentifier such as a VLAN ID, VXLAN Network Identifier (VNID), NVGREVirtual Subnet Identifier (VSID), MAC address, IP address, etc.

In some cases, classification in the ACI infrastructure can beimplemented by Application Virtual Switches (AVS), which can run on ahost, such as a server or switch. For example, an AVS can classifytraffic based on specified attributes, and tag packets of differentattribute EPGs with different identifiers, such as network segmentidentifiers (e.g., VLAN ID). Finally, Leafs 104 can tie packets withtheir attribute EPGs based on their identifiers and enforce policies,which can be implemented and/or managed by one or more Controllers 116.Leaf 104 can classify to which EPG the traffic from a host belongs andenforce policies accordingly.

Another example SDN solution is based on VMWARE NSX. With VMWARE NSX,hosts can run a distributed firewall (DFW) which can classify andprocess traffic. Consider a case where three types of VMs, namely,application, database and web VMs, are put into a single layer-2 networksegment. Traffic protection can be provided within the network segmentbased on the VM type. For example, HTTP traffic can be permitted amongweb VMs, and not permitted between a web VM and an application ordatabase VM. To classify traffic and implement policies, VMWARE NSX canimplement security groups, which can be used to group the specific VMs(e.g., web VMs, application VMs, database VMs). DFW rules can beconfigured to implement policies for the specific security groups. Toillustrate, in the context of the previous example, DFW rules can beconfigured to block HTTP traffic between web, application, and databasesecurity groups.

Returning now to FIG. 1A, Network Environment 100 can deploy differenthosts via Leafs 104, Servers 106, Hypervisors 108, VMs 110, Applications112, and Controllers 116, such as VMWARE ESXi hosts, WINDOWS HYPER-Vhosts, bare metal physical hosts, etc. Network Environment 100 mayinteroperate with a variety of Hypervisors 108, Servers 106 (e.g.,physical and/or virtual servers), SDN orchestration platforms, etc.Network Environment 100 may implement a declarative model to allow itsintegration with application design and holistic network policy.

Controllers 116 can provide centralized access to fabric information,application configuration, resource configuration, application-levelconfiguration modeling for a software-defined network (SDN)infrastructure, integration with management systems or servers, etc.Controllers 116 can form a control plane that interfaces with anapplication plane via northbound APIs and a data plane via southboundAPIs.

As previously noted, Controllers 116 can define and manageapplication-level model(s) for configurations in Network Environment100. In some cases, application or device configurations can also bemanaged and/or defined by other components in the network. For example,a hypervisor or virtual appliance, such as a VM or container, can run aserver or management tool to manage software and services in NetworkEnvironment 100, including configurations and settings for virtualappliances.

As illustrated above, Network Environment 100 can include one or moredifferent types of SDN solutions, hosts, etc. For the sake of clarityand explanation purposes, various examples in the disclosure will bedescribed with reference to an ACI framework, and Controllers 116 may beinterchangeably referenced as controllers, APICs, or APIC controllers.However, it should be noted that the technologies and concepts hereinare not limited to ACI solutions and may be implemented in otherarchitectures and scenarios, including other SDN solutions as well asother types of networks which may not deploy an SDN solution.

Further, as referenced herein, the term “hosts” can refer to Servers 106(e.g., physical or logical), Hypervisors 108, VMs 110, containers (e.g.,Applications 112), etc., and can run or include any type of server orapplication solution. Non-limiting examples of “hosts” can includevirtual switches or routers, such as distributed virtual switches (DVS),application virtual switches (AVS), vector packet processing (VPP)switches; VCENTER and NSX MANAGERS; bare metal physical hosts; HYPER-Vhosts; VMs; DOCKER Containers; etc.

FIG. 1B illustrates another example of Network Environment 100. In thisexample, Network Environment 100 includes Endpoints 122 connected toLeafs 104 in Fabric 120. Endpoints 122 can be physical and/or logical orvirtual entities, such as servers, clients, VMs, hypervisors, softwarecontainers, applications, resources, network devices, workloads, etc.For example, an Endpoint 122 can be an object that represents a physicaldevice (e.g., server, client, switch, etc.), an application (e.g., webapplication, database application, etc.), a logical or virtual resource(e.g., a virtual switch, a virtual service appliance, a virtualizednetwork function (VNF), a VM, a service chain, etc.), a containerrunning a software resource (e.g., an application, an appliance, a VNF,a service chain, etc.), storage, a workload or workload engine, etc.Endpoints 122 can have an address (e.g., an identity), a location (e.g.,host, network segment, virtual routing and forwarding (VRF) instance,domain, etc.), one or more attributes (e.g., name, type, version, patchlevel, OS name, OS type, etc.), a tag (e.g., security tag), a profile,etc.

Endpoints 122 can be associated with respective Logical Groups 118.Logical Groups 118 can be logical entities containing endpoints(physical and/or logical or virtual) grouped together according to oneor more attributes, such as endpoint type (e.g., VM type, workload type,application type, etc.), one or more requirements (e.g., policyrequirements, security requirements, QoS requirements, customerrequirements, resource requirements, etc.), a resource name (e.g., VMname, application name, etc.), a profile, platform or operating system(OS) characteristics (e.g., OS type or name including guest and/or hostOS, etc.), an associated network or tenant, one or more policies, a tag,etc. For example, a logical group can be an object representing acollection of endpoints grouped together. To illustrate, Logical Group 1can contain client endpoints, Logical Group 2 can contain web serverendpoints, Logical Group 3 can contain application server endpoints,Logical Group N can contain database server endpoints, etc. In someexamples, Logical Groups 118 are EPGs in an ACI environment and/or otherlogical groups (e.g., SGs) in another SDN environment.

Traffic to and/or from Endpoints 122 can be classified, processed,managed, etc., based Logical Groups 118. For example, Logical Groups 118can be used to classify traffic to or from Endpoints 122, apply policiesto traffic to or from Endpoints 122, define relationships betweenEndpoints 122, define roles of Endpoints 122 (e.g., whether an endpointconsumes or provides a service, etc.), apply rules to traffic to or fromEndpoints 122, apply filters or access control lists (ACLs) to trafficto or from Endpoints 122, define communication paths for traffic to orfrom Endpoints 122, enforce requirements associated with Endpoints 122,implement security and other configurations associated with Endpoints122, etc.

In an ACI environment, Logical Groups 118 can be EPGs used to definecontracts in the ACI. Contracts can include rules specifying what andhow communications between EPGs take place. For example, a contract candefine what provides a service, what consumes a service, and what policyobjects are related to that consumption relationship. A contract caninclude a policy that defines the communication path and all relatedelements of a communication or relationship between endpoints or EPGs.For example, a Web EPG can provide a service that a Client EPG consumes,and that consumption can be subject to a filter (ACL) and a servicegraph that includes one or more services, such as firewall inspectionservices and server load balancing.

FIG. 2A illustrates a diagram of an example Management Information Model200 for an SDN network, such as Network Environment 100. The followingdiscussion of Management Information Model 200 references various termswhich shall also be used throughout the disclosure. Accordingly, forclarity, the disclosure shall first provide below a list of terminology,which will be followed by a more detailed discussion of ManagementInformation Model 200.

As used herein, an “Alias” can refer to a changeable name for a givenobject. Thus, even if the name of an object, once created, cannot bechanged, the Alias can be a field that can be changed.

As used herein, the terms “Aliasing” and “Shadowing” can refer to a rule(e.g., contracts, policies, configurations, etc.) that overlaps one ormore other rules. For example, Contract 1 defined in a logical model ofa network can be said to be aliasing or shadowing Contract 2 defined inthe logical model of the network if Contract 1 overlaps Contract 1. Inthis example, by aliasing or shadowing Contract 2, Contract 1 may renderContract 2 redundant or inoperable. For example, if Contract 1 has ahigher priority than Contract 2, such aliasing can render Contract 2redundant based on Contract 1's overlapping and higher prioritycharacteristics.

As used herein, the term “APIC” can refer to one or more controllers(e.g., Controllers 116) in an ACI framework. The APIC can provide aunified point of automation and management, policy programming,application deployment, health monitoring for an ACI multitenant fabric.The APIC can be implemented as a single controller, a distributedcontroller, or a replicated, synchronized, and/or clustered controller.

As used herein, the term “BDD” can refer to a binary decision tree. Abinary decision tree can be a data structure representing functions,such as Boolean functions.

As used herein, the term “BD” can refer to a bridge domain. A bridgedomain can be a set of logical ports that share the same flooding orbroadcast characteristics. Like a virtual LAN (VLAN), bridge domains canspan multiple devices. A bridge domain can be a L2 (Layer 2) construct.

As used herein, a “Consumer” can refer to an endpoint, resource, and/orEPG that consumes a service.

As used herein, a “Context” can refer to an L3 (Layer 3) address domainthat permits multiple instances of a routing table to exist and worksimultaneously. This increases functionality by permitting network pathsto be segmented without using multiple devices. Non-limiting examples ofa context or L3 address domain can include a Virtual Routing andForwarding (VRF) instance, a private network, and so forth.

As used herein, the term “Contract” can refer to rules or configurationsthat specify what and how communications in a network are conducted(e.g., permitted, denied, filtered, processed, etc.). In an ACI network,contracts can specify how communications between endpoints and/or EPGstake place. In some examples, a contract can provide rules andconfigurations akin to an Access Control List (ACL).

As used herein, the term “Distinguished Name” (DN) can refer to a uniquename that describes an object, such as an MO, and locates its place inManagement Information Model 200. In some cases, the DN can be (orequate to) a Fully Qualified Domain Name (FQDN).

As used herein, the term “Endpoint Group” (EPG) can refer to a logicalentity or object associated with a collection or group of endoints aspreviously described with reference to FIG. 1B.

As used herein, the term “Filter” can refer to a parameter orconfiguration for permitting communications. For example, in a whitelistmodel where all communications are blocked by default, a communicationmust be given explicit permission to prevent such communication frombeing blocked. A filter can define permission(s) for one or morecommunications or packets. A filter can thus function similar to an ACLor Firewall rule. In some examples, a filter can be implemented in apacket (e.g., TCP/IP) header field, such as L3 protocol type, L4 (Layer4) ports, and so on, which is used to permit inbound or outboundcommunications between endpoints or EPGs, for example.

As used herein, the term “L2 Out” can refer to a bridged connection. Abridged connection can connect two or more segments of the same networkso that they can communicate. In an ACI framework, an L2 out can be abridged (Layer 2) connection between an ACI fabric (e.g., Fabric 120)and an outside Layer 2 network, such as a switch.

As used herein, the term “L3 Out” can refer to a routed connection. Arouted Layer 3 connection uses a set of protocols that determine thepath that data follows in order to travel across networks from itssource to its destination. Routed connections can perform forwarding(e.g., IP forwarding) according to a protocol selected, such as BGP(border gateway protocol), OSPF (Open Shortest Path First), EIGRP(Enhanced Interior Gateway Routing Protocol), etc.

As used herein, the term “Managed Object” (MO) can refer to an abstractrepresentation of objects that are managed in a network (e.g., NetworkEnvironment 100). The objects can be concrete objects (e.g., a switch,server, adapter, etc.), or logical objects (e.g., an applicationprofile, an EPG, a fault, etc.). The MOs can be network resources orelements that are managed in the network. For example, in an ACIenvironment, an MO can include an abstraction of an ACI fabric (e.g.,Fabric 120) resource.

As used herein, the term “Management Information Tree” (MIT) can referto a hierarchical management information tree containing the MOs of asystem. For example, in ACI, the MIT contains the MOs of the ACI fabric(e.g., Fabric 120). The MIT can also be referred to as a ManagementInformation Model (MIM), such as Management Information Model 200.

As used herein, the term “Policy” can refer to one or morespecifications for controlling some aspect of system or networkbehavior. For example, a policy can include a named entity that containsspecifications for controlling some aspect of system behavior. Toillustrate, a Layer 3 Outside Network Policy can contain the BGPprotocol to enable BGP routing functions when connecting Fabric 120 toan outside Layer 3 network.

As used herein, the term “Profile” can refer to the configurationdetails associated with a policy. For example, a profile can include anamed entity that contains the configuration details for implementingone or more instances of a policy. To illustrate, a switch node profilefor a routing policy can contain the switch-specific configurationdetails to implement the BGP routing protocol.

As used herein, the term “Provider” refers to an object or entityproviding a service. For example, a provider can be an EPG that providesa service.

As used herein, the term “Subject” refers to one or more parameters in acontract for defining communications. For example, in ACI, subjects in acontract can specify what information can be communicated and how.Subjects can function similar to ACLs.

As used herein, the term “Tenant” refers to a unit of isolation in anetwork. For example, a tenant can be a secure and exclusive virtualcomputing environment. In ACI, a tenant can be a unit of isolation froma policy perspective, but does not necessarily represent a privatenetwork. Indeed, ACI tenants can contain multiple private networks(e.g., VRFs). Tenants can represent a customer in a service providersetting, an organization or domain in an enterprise setting, or just agrouping of policies.

As used herein, the term “VRF” refers to a virtual routing andforwarding instance. The VRF can define a Layer 3 address domain thatpermits multiple instances of a routing table to exist and worksimultaneously. This increases functionality by permitting network pathsto be segmented without using multiple devices. Also known as a contextor private network.

Having described various terms used herein, the disclosure now returnsto a discussion of Management Information Model (MIM) 200 in FIG. 2A. Aspreviously noted, MIM 200 can be a hierarchical management informationtree or MIT. Moreover, MIM 200 can be managed and processed byControllers 116, such as APICs in an ACI. Controllers 116 can enable thecontrol of managed resources by presenting their manageablecharacteristics as object properties that can be inherited according tothe location of the object within the hierarchical structure of themodel.

The hierarchical structure of MIM 200 starts with Policy Universe 202 atthe top (Root) and contains parent and child nodes 116, 204, 206, 208,210, 212. Nodes 116, 202, 204, 206, 208, 210, 212 in the tree representthe managed objects (MOs) or groups of objects. Each object in thefabric (e.g., Fabric 120) has a unique distinguished name (DN) thatdescribes the object and locates its place in the tree. The Nodes 116,202, 204, 206, 208, 210, 212 can include the various MOs, as describedbelow, which contain policies that govern the operation of the system.

Controllers 116

Controllers 116 (e.g., APIC controllers) can provide management, policyprogramming, application deployment, and health monitoring for Fabric120.

Node 204

Node 204 includes a tenant container for policies that enable anadministrator to exercise domain-based access control. Non-limitingexamples of tenants can include:

User tenants defined by the administrator according to the needs ofusers. They contain policies that govern the operation of resources suchas applications, databases, web servers, network-attached storage,virtual machines, and so on.

The common tenant is provided by the system but can be configured by theadministrator. It contains policies that govern the operation ofresources accessible to all tenants, such as firewalls, load balancers,Layer 4 to Layer 7 services, intrusion detection appliances, and so on.

The infrastructure tenant is provided by the system but can beconfigured by the administrator. It contains policies that govern theoperation of infrastructure resources such as the fabric overlay (e.g.,VXLAN). It also enables a fabric provider to selectively deployresources to one or more user tenants. Infrastructure tenant polices canbe configurable by the administrator.

The management tenant is provided by the system but can be configured bythe administrator. It contains policies that govern the operation offabric management functions used for in-band and out-of-bandconfiguration of fabric nodes. The management tenant contains a privateout-of-bound address space for the Controller/Fabric internalcommunications that is outside the fabric data path that provides accessthrough the management port of the switches. The management tenantenables discovery and automation of communications with virtual machinecontrollers.

Node 206

Node 206 can contain access policies that govern the operation of switchaccess ports that provide connectivity to resources such as storage,compute, Layer 2 and Layer 3 (bridged and routed) connectivity, virtualmachine hypervisors, Layer 4 to Layer 7 devices, and so on. If a tenantrequires interface configurations other than those provided in thedefault link, Cisco Discovery Protocol (CDP), Link Layer DiscoveryProtocol (LLDP), Link Aggregation Control Protocol (LACP), or SpanningTree Protocol (STP), an administrator can configure access policies toenable such configurations on the access ports of Leafs 104.

Node 206 can contain fabric policies that govern the operation of theswitch fabric ports, including such functions as Network Time Protocol(NTP) server synchronization, Intermediate System-to-Intermediate SystemProtocol (IS-IS), Border Gateway Protocol (BGP) route reflectors, DomainName System (DNS) and so on. The fabric MO contains objects such aspower supplies, fans, chassis, and so on.

Node 208

Node 208 can contain VM domains that group VM controllers with similarnetworking policy requirements. VM controllers can share virtual space(e.g., VLAN or VXLAN space) and application EPGs. Controllers 116communicate with the VM controller to publish network configurationssuch as port groups that are then applied to the virtual workloads.

Node 210

Node 210 can contain Layer 4 to Layer 7 service integration life cycleautomation framework that enables the system to dynamically respond whena service comes online or goes offline. Policies can provide servicedevice package and inventory management functions.

Node 212

Node 212 can contain access, authentication, and accounting (AAA)policies that govern user privileges, roles, and security domains ofFabric 120.

The hierarchical policy model can fit well with an API, such as a RESTAPI interface. When invoked, the API can read from or write to objectsin the MIT. URLs can map directly into distinguished names that identifyobjects in the MIT. Data in the MIT can be described as a self-containedstructured tree text document encoded in XML or JSON, for example.

FIG. 2B illustrates an example object model 220 for a tenant portion ofMIM 200. As previously noted, a tenant is a logical container forapplication policies that enable an administrator to exercisedomain-based access control. A tenant thus represents a unit ofisolation from a policy perspective, but it does not necessarilyrepresent a private network. Tenants can represent a customer in aservice provider setting, an organization or domain in an enterprisesetting, or just a convenient grouping of policies. Moreover, tenantscan be isolated from one another or can share resources.

Tenant portion 204A of MIM 200 can include various entities, and theentities in Tenant Portion 204A can inherit policies from parententities. Non-limiting examples of entities in Tenant Portion 204A caninclude Filters 240, Contracts 236, Outside Networks 222, Bridge Domains230, VRF Instances 234, and Application Profiles 224.

Bridge Domains 230 can include Subnets 232. Contracts 236 can includeSubjects 238. Application Profiles 224 can contain one or more EPGs 226.Some applications can contain multiple components. For example, ane-commerce application could require a web server, a database server,data located in a storage area network, and access to outside resourcesthat enable financial transactions. Application Profile 224 contains asmany (or as few) EPGs as necessary that are logically related toproviding the capabilities of an application.

EPG 226 can be organized in various ways, such as based on theapplication they provide, the function they provide (such asinfrastructure), where they are in the structure of the data center(such as DMZ), or whatever organizing principle that a fabric or tenantadministrator chooses to use.

EPGs in the fabric can contain various types of EPGs, such asapplication EPGs, Layer 2 external outside network instance EPGs, Layer3 external outside network instance EPGs, management EPGs forout-of-band or in-band access, etc. EPGs 226 can also contain Attributes228, such as encapsulation-based EPGs, IP-based EPGs, or MAC-based EPGs.

As previously mentioned, EPGs can contain endpoints (e.g., EPs 122) thathave common characteristics or attributes, such as common policyrequirements (e.g., security, virtual machine mobility (VMM), QoS, orLayer 4 to Layer 7 services). Rather than configure and manage endpointsindividually, they can be placed in an EPG and managed as a group.

Policies apply to EPGs, including the endpoints they contain. An EPG canbe statically configured by an administrator in Controllers 116, ordynamically configured by an automated system such as VCENTER orOPENSTACK.

To activate tenant policies in Tenant Portion 204A, fabric accesspolicies should be configured and associated with tenant policies.Access policies enable an administrator to configure other networkconfigurations, such as port channels and virtual port channels,protocols such as LLDP, CDP, or LACP, and features such as monitoring ordiagnostics.

FIG. 2C illustrates an example Association 260 of tenant entities andaccess entities in MIM 200. Policy Universe 202 contains Tenant Portion204A and Access Portion 206A. Thus, Tenant Portion 204A and AccessPortion 206A are associated through Policy Universe 202.

Access Portion 206A can contain fabric and infrastructure accesspolicies. Typically, in a policy model, EPGs are coupled with VLANs. Fortraffic to flow, an EPG is deployed on a leaf port with a VLAN in aphysical, VMM, L2 out, L3 out, or Fiber Channel domain, for example.

Access Portion 206A thus contains Domain Profile 236 which can define aphysical, VMM, L2 out, L3 out, or Fiber Channel domain, for example, tobe associated to the EPGs. Domain Profile 236 contains VLAN InstanceProfile 238 (e.g., VLAN pool) and Attacheable Access Entity Profile(AEP) 240, which are associated directly with application EPGs. The AEP240 deploys the associated application EPGs to the ports to which it isattached, and automates the task of assigning VLANs. While a large datacenter can have thousands of active VMs provisioned on hundreds ofVLANs, Fabric 120 can automatically assign VLAN IDs from VLAN pools.This saves time compared with trunking down VLANs in a traditional datacenter.

FIG. 2D illustrates a schematic diagram of example models for a network,such as Network Environment 100. The models can be generated based onspecific configurations and/or network state parameters associated withvarious objects, policies, properties, and elements defined in MIM 200.The models can be implemented for network analysis and assurance, andmay provide a depiction of the network at various stages ofimplementation and levels of the network.

As illustrated, the models can include L_Model 270A (Logical Model),LR_Model 270B (Logical Rendered Model or Logical Runtime Model),Li_Model 272 (Logical Model for i), Ci_Model 274 (Concrete model for i),and/or Hi_Model 276 (Hardware model or TCAM Model for i).

L_Model 270A is the logical representation of various elements in MIM200 as configured in a network (e.g., Network Environment 100), such asobjects, object properties, object relationships, and other elements inMIM 200 as configured in a network. L_Model 270A can be generated byControllers 116 based on configurations entered in Controllers 116 forthe network, and thus represents the logical configuration of thenetwork at Controllers 116. This is the declaration of the “end-state”expression that is desired when the elements of the network entities(e.g., applications, tenants, etc.) are connected and Fabric 120 isprovisioned by Controllers 116. Because L_Model 270A represents theconfigurations entered in Controllers 116, including the objects andrelationships in MIM 200, it can also reflect the “intent” of theadministrator: how the administrator wants the network and networkelements to behave.

L_Model 270A can be a fabric or network-wide logical model. For example,L_Model 270A can account configurations and objects from each ofControllers 116. As previously explained, Network Environment 100 caninclude multiple Controllers 116. In some cases, two or more Controllers116 may include different configurations or logical models for thenetwork. In such cases, L_Model 270A can obtain any of theconfigurations or logical models from Controllers 116 and generate afabric or network wide logical model based on the configurations andlogical models from all Controllers 116. L_Model 270A can thusincorporate configurations or logical models between Controllers 116 toprovide a comprehensive logical model. L_Model 270A can also address oraccount for any dependencies, redundancies, conflicts, etc., that mayresult from the configurations or logical models at the differentControllers 116.

LR_Model 270B is the abstract model expression that Controllers 116(e.g., APICs in ACI) resolve from L_Model 270A. LR_Model 270B canprovide the configuration components that would be delivered to thephysical infrastructure (e.g., Fabric 120) to execute one or morepolicies. For example, LR_Model 270B can be delivered to Leafs 104 inFabric 120 to configure Leafs 104 for communication with attachedEndpoints 122. LR_Model 270B can also incorporate state information tocapture a runtime state of the network (e.g., Fabric 120).

In some cases, LR_Model 270B can provide a representation of L_Model270A that is normalized according to a specific format or expressionthat can be propagated to, and/or understood by, the physicalinfrastructure of Fabric 120 (e.g., Leafs 104, Spines 102, etc.). Forexample, LR_Model 270B can associate the elements in L_Model 270A withspecific identifiers or tags that can be interpreted and/or compiled bythe switches in Fabric 120, such as hardware plane identifiers used asclassifiers.

Li_Model 272 is a switch-level or switch-specific model obtained fromL_Model 270A and/or LR_Model 270B. Li_Model 272 can project L_Model 270Aand/or LR_Model 270B on a specific switch or device i, and thus canconvey how L_Model 270A and/or LR_Model 270B should appear or beimplemented at the specific switch or device i.

For example, Li_Model 272 can project L_Model 270A and/or LR_Model 270Bpertaining to a specific switch i to capture a switch-levelrepresentation of L_Model 270A and/or LR_Model 270B at switch i. Toillustrate, Li_Model 272 L₁ can represent L_Model 270A and/or LR_Model270B projected to, or implemented at, Leaf 1 (104). Thus, Li_Model 272can be generated from L_Model 270A and/or LR_Model 270B for individualdevices (e.g., Leafs 104, Spines 102, etc.) on Fabric 120.

In some cases, Li_Model 272 can be represented using JSON (JavaScriptObject Notation). For example, Li_Model 272 can include JSON objects,such as Rules, Filters, Entries, and Scopes.

Ci_Model 274 is the actual in-state configuration at the individualfabric member i (e.g., switch i). In other words, Ci_Model 274 is aswitch-level or switch-specific model that is based on Li_Model 272. Forexample, Controllers 116 can deliver Li_Model 272 to Leaf 1 (104). Leaf1 (104) can take Li_Model 272, which can be specific to Leaf 1 (104),and render the policies in Li_Model 272 into a concrete model, Ci_Model274, that runs on Leaf 1 (104). Leaf 1 (104) can render Li_Model 272 viathe OS on Leaf 1 (104), for example. Thus, Ci_Model 274 can be analogousto compiled software, as it is the form of Li_Model 272 that the switchOS at Leaf 1 (104) can execute.

In some cases, Li_Model 272 and Ci_Model 274 can have a same or similarformat. For example, Li_Model 272 and Ci_Model 274 can be based on JSONobjects. Having the same or similar format can facilitate objects inLi_Model 272 and Ci_Model 274 to be compared for equivalence orcongruence. Such equivalence or congruence checks can be used fornetwork analysis and assurance, as further described herein.

Hi_Model 276 is also a switch-level or switch-specific model for switchi, but is based on Ci_Model 274 for switch i. Hi_Model 276 is the actualconfiguration (e.g., rules) stored or rendered on the hardware or memory(e.g., TCAM memory) at the individual fabric member i (e.g., switch i).For example, Hi_Model 276 can represent the configurations (e.g., rules)which Leaf 1 (104) stores or renders on the hardware (e.g., TCAM memory)of Leaf 1 (104) based on Ci_Model 274 at Leaf 1 (104). The switch OS atLeaf 1 (104) can render or execute Ci_Model 274, and Leaf 1 (104) canstore or render the configurations from Ci_Model 274 in storage, such asthe memory or TCAM at Leaf 1 (104). The configurations from Hi_Model 276stored or rendered by Leaf 1 (104) represent the configurations thatwill be implemented by Leaf 1 (104) when processing traffic.

While Models 272, 274, 276 are shown as device-specific models, similarmodels can be generated or aggregated for a collection of fabric members(e.g., Leafs 104 and/or Spines 102) in Fabric 120. When combined,device-specific models, such as Model 272, Model 274, and/or Model 276,can provide a representation of Fabric 120 that extends beyond aparticular device. For example, in some cases, Li_Model 272, Ci_Model274, and/or Hi_Model 276 associated with some or all individual fabricmembers (e.g., Leafs 104 and Spines 102) can be combined or aggregatedto generate one or more aggregated models based on the individual fabricmembers.

As referenced herein, the terms H Model, T Model, and TCAM Model can beused interchangeably to refer to a hardware model, such as Hi_Model 276.For example, Ti Model, Hi Model and TCAMi Model may be usedinterchangeably to refer to Hi_Model 276.

Models 270A, 270B, 272, 274, 276 can provide representations of variousaspects of the network or various configuration stages for MIM 200. Forexample, one or more of Models 270A, 270B, 272, 274, 276 can be used togenerate Underlay Model 278 representing one or more aspects of Fabric120 (e.g., underlay topology, routing, etc.), Overlay Model 280representing one or more aspects of the overlay or logical segment(s) ofNetwork Environment 100 (e.g., COOP, MPBGP, tenants, VRFs, VLANs,VXLANs, virtual applications, VMs, hypervisors, virtual switching,etc.), Tenant Model 282 representing one or more aspects of Tenantportion 204A in MIM 200 (e.g., security, forwarding, service chaining,QoS, VRFs, BDs, Contracts, Filters, EPGs, subnets, etc.), ResourcesModel 284 representing one or more resources in Network Environment 100(e.g., storage, computing, VMs, port channels, physical elements, etc.),etc.

In general, L_Model 270A can be the high-level expression of what existsin the LR_Model 270B, which should be present on the concrete devices asCi_Model 274 and Hi_Model 276 expression. If there is any gap betweenthe models, there may be inconsistent configurations or problems.

FIG. 3A illustrates a diagram of an example Assurance Appliance 300 fornetwork assurance. In this example, Assurance Appliance 300 can includek VMs 110 operating in cluster mode. VMs are used in this example forexplanation purposes. However, it should be understood that otherconfigurations are also contemplated herein, such as use of containers,bare metal devices, Endpoints 122, or any other physical or logicalsystems. Moreover, while FIG. 3A illustrates a cluster modeconfiguration, other configurations are also contemplated herein, suchas a single mode configuration (e.g., single VM, container, or server)or a service chain for example.

Assurance Appliance 300 can run on one or more Servers 106, VMs 110,Hypervisors 108, EPs 122, Leafs 104, Controllers 116, or any othersystem or resource. For example, Assurance Appliance 300 can be alogical service or application running on one or more VMs 110 in NetworkEnvironment 100.

The Assurance Appliance 300 can include Data Framework 308, which can bebased on, for example, APACHE APEX and HADOOP. In some cases, assurancechecks can be written as individual operators that reside in DataFramework 308. This enables a natively horizontal scale-out architecturethat can scale to arbitrary number of switches in Fabric 120 (e.g., ACIfabric).

Assurance Appliance 300 can poll Fabric 120 at a configurableperiodicity (e.g., an epoch). The analysis workflow can be setup as aDAG (Directed Acyclic Graph) of Operators 310, where data flows from oneoperator to another and eventually results are generated and persistedto Database 302 for each interval (e.g., each epoch).

The north-tier implements API Server (e.g., APACHE Tomcat and Springframework) 304 and Web Server 306. A graphical user interface (GUI)interacts via the APIs exposed to the customer. These APIs can also beused by the customer to collect data from Assurance Appliance 300 forfurther integration into other tools.

Operators 310 in Data Framework 308 (e.g., APEX/Hadoop) can togethersupport assurance operations. Below are non-limiting examples ofassurance operations that can be performed by Assurance Appliance 300via Operators 310.

Security Policy Adherence

Assurance Appliance 300 can check to make sure the configurations orspecification from L_Model 270A, which may reflect the user's intent forthe network, including for example the security policies andcustomer-configured contracts, are correctly implemented and/or renderedin Li_Model 272, Ci_Model 274, and Hi_Model 276, and thus properlyimplemented and rendered by the fabric members (e.g., Leafs 104), andreport any errors, contract violations, or irregularities found.

Static Policy Analysis

Assurance Appliance 300 can check for issues in the specification of theuser's intent or intents (e.g., identify contradictory or conflictingpolicies in L_Model 270A).

TCAM Utilization

TCAM is a scarce resource in the fabric (e.g., Fabric 120). However,Assurance Appliance 300 can analyze the TCAM utilization by the networkdata (e.g., Longest Prefix Match (LPM) tables, routing tables, VLANtables, BGP updates, etc.), Contracts, Logical Groups 118 (e.g., EPGs),Tenants, Spines 102, Leafs 104, and other dimensions in NetworkEnvironment 100 and/or objects in MIM 200, to provide a network operatoror user visibility into the utilization of this scarce resource. Thiscan greatly help for planning and other optimization purposes.

Endpoint Checks

Assurance Appliance 300 can validate that the fabric (e.g. fabric 120)has no inconsistencies in the Endpoint information registered (e.g., twoleafs announcing the same endpoint, duplicate subnets, etc.), amongother such checks.

Tenant Routing Checks

Assurance Appliance 300 can validate that BDs, VRFs, subnets (bothinternal and external), VLANs, contracts, filters, applications, EPGs,etc., are correctly programmed.

Infrastructure Routing

Assurance Appliance 300 can validate that infrastructure routing (e.g.,IS-IS protocol) has no convergence issues leading to black holes, loops,flaps, and other problems.

MP-BGP Route Reflection Checks

The network fabric (e.g., Fabric 120) can interface with other externalnetworks and provide connectivity to them via one or more protocols,such as Border Gateway Protocol (BGP), Open Shortest Path First (OSPF),etc. The learned routes are advertised within the network fabric via,for example, MP-BGP. These checks can ensure that a route reflectionservice via, for example, MP-BGP (e.g., from Border Leaf) does not havehealth issues.

Logical Lint and Real-Time Change Analysis

Assurance Appliance 300 can validate rules in the specification of thenetwork (e.g., L_Model 270A) are complete and do not haveinconsistencies or other problems. MOs in the MIM 200 can be checked byAssurance Appliance 300 through syntactic and semantic checks performedon L_Model 270A and/or the associated configurations of the MOs in MIM200. Assurance Appliance 300 can also verify that unnecessary, stale,unused or redundant configurations, such as contracts, are removed.

FIG. 3B illustrates an architectural diagram of an example system 350for network assurance, such as Assurance Appliance 300. In some cases,system 350 can correspond to the DAG of Operators 310 previouslydiscussed with respect to FIG. 3A

In this example, Topology Explorer 312 communicates with Controllers 116(e.g., APIC controllers) in order to discover or otherwise construct acomprehensive topological view of Fabric 120 (e.g., Spines 102, Leafs104, Controllers 116, Endpoints 122, and any other components as well astheir interconnections). While various architectural components arerepresented in a singular, boxed fashion, it is understood that a givenarchitectural component, such as Topology Explorer 312, can correspondto one or more individual Operators 310 and may include one or morenodes or endpoints, such as one or more servers, VMs, containers,applications, service functions (e.g., functions in a service chain orvirtualized network function), etc.

Topology Explorer 312 is configured to discover nodes in Fabric 120,such as Controllers 116, Leafs 104, Spines 102, etc. Topology Explorer312 can additionally detect a majority election performed amongstControllers 116, and determine whether a quorum exists amongstControllers 116. If no quorum or majority exists, Topology Explorer 312can trigger an event and alert a user that a configuration or othererror exists amongst Controllers 116 that is preventing a quorum ormajority from being reached. Topology Explorer 312 can detect Leafs 104and Spines 102 that are part of Fabric 120 and publish theircorresponding out-of-band management network addresses (e.g., IPaddresses) to downstream services. This can be part of the topologicalview that is published to the downstream services at the conclusion ofTopology Explorer's 312 discovery epoch (e.g., 5 minutes, or some otherspecified interval).

In some examples, Topology Explorer 312 can receive as input a list ofControllers 116 (e.g., APIC controllers) that are associated with thenetwork/fabric (e.g., Fabric 120). Topology Explorer 312 can alsoreceive corresponding credentials to login to each controller. TopologyExplorer 312 can retrieve information from each controller using, forexample, REST calls. Topology Explorer 312 can obtain from eachcontroller a list of nodes (e.g., Leafs 104 and Spines 102), and theirassociated properties, that the controller is aware of Topology Explorer312 can obtain node information from Controllers 116 including, withoutlimitation, an IP address, a node identifier, a node name, a nodedomain, a node URI, a node dm, a node role, a node version, etc.

Topology Explorer 312 can also determine if Controllers 116 are inquorum, or are sufficiently communicatively coupled amongst themselves.For example, if there are n controllers, a quorum condition might be metwhen (n/2+1) controllers are aware of each other and/or arecommunicatively coupled. Topology Explorer 312 can make thedetermination of a quorum (or identify any failed nodes or controllers)by parsing the data returned from the controllers, and identifyingcommunicative couplings between their constituent nodes. TopologyExplorer 312 can identify the type of each node in the network, e.g.spine, leaf, APIC, etc., and include this information in the topologyinformation generated (e.g., topology map or model).

If no quorum is present, Topology Explorer 312 can trigger an event andalert a user that reconfiguration or suitable attention is required. Ifa quorum is present, Topology Explorer 312 can compile the networktopology information into a JSON object and pass it downstream to otheroperators or services, such as Unified Collector 314.

Unified Collector 314 can receive the topological view or model fromTopology Explorer 312 and use the topology information to collectinformation for network assurance from Fabric 120. Unified Collector 314can poll nodes (e.g., Controllers 116, Leafs 104, Spines 102, etc.) inFabric 120 to collect information from the nodes.

Unified Collector 314 can include one or more collectors (e.g.,collector devices, operators, applications, VMs, etc.) configured tocollect information from Topology Explorer 312 and/or nodes in Fabric120. For example, Unified Collector 314 can include a cluster ofcollectors, and each of the collectors can be assigned to a subset ofnodes within the topological model and/or Fabric 120 in order to collectinformation from their assigned subset of nodes. For performance,Unified Collector 314 can run in a parallel, multi-threaded fashion.

Unified Collector 314 can perform load balancing across individualcollectors in order to streamline the efficiency of the overallcollection process. Load balancing can be optimized by managing thedistribution of subsets of nodes to collectors, for example by randomlyhashing nodes to collectors.

In some cases, Assurance Appliance 300 can run multiple instances ofUnified Collector 314. This can also allow Assurance Appliance 300 todistribute the task of collecting data for each node in the topology(e.g., Fabric 120 including Spines 102, Leafs 104, Controllers 116,etc.) via sharding and/or load balancing, and map collection tasksand/or nodes to a particular instance of Unified Collector 314 with datacollection across nodes being performed in parallel by various instancesof Unified Collector 314. Within a given node, commands and datacollection can be executed serially. Assurance Appliance 300 can controlthe number of threads used by each instance of Unified Collector 314 topoll data from Fabric 120.

Unified Collector 314 can collect models (e.g., L_Model 270A and/orLR_Model 270B) from Controllers 116, switch software configurations andmodels (e.g., Ci_Model 274) from nodes (e.g., Leafs 104 and/or Spines102) in Fabric 120, hardware configurations and models (e.g., Hi_Model276) from nodes (e.g., Leafs 104 and/or Spines 102) in Fabric 120, etc.Unified Collector 314 can collect Ci_Model 274 and Hi_Model 276 fromindividual nodes or fabric members, such as Leafs 104 and Spines 102,and L_Model 270A and/or LR_Model 270B from one or more controllers(e.g., Controllers 116) in Network Environment 100.

Unified Collector 314 can poll the devices that Topology Explorer 312discovers in order to collect data from Fabric 120 (e.g., from theconstituent members of the fabric). Unified Collector 314 can collectthe data using interfaces exposed by Controllers 116 and/or switchsoftware (e.g., switch OS), including, for example, a RepresentationState Transfer (REST) Interface and a Secure Shell (SSH) Interface.

In some cases, Unified Collector 314 collects L_Model 270A, LR_Model270B, and/or Ci_Model 274 via a REST API, and the hardware information(e.g., configurations, tables, fabric card information, rules, routes,etc.) via SSH using utilities provided by the switch software, such asvirtual shell (VSH or VSHELL) for accessing the switch command-lineinterface (CLI) or VSH_LC shell for accessing runtime state of the linecard.

Unified Collector 314 can poll other information from Controllers 116,including, without limitation: topology information, tenantforwarding/routing information, tenant security policies, contracts,interface policies, physical domain or VMM domain information, OOB(out-of-band) management IP's of nodes in the fabric, etc.

Unified Collector 314 can also poll information from nodes (e.g., Leafs104 and Spines 102) in Fabric 120, including without limitation:Ci_Models 274 for VLANs, BDs, and security policies; Link LayerDiscovery Protocol (LLDP) connectivity information of nodes (e.g., Leafs104 and/or Spines 102); endpoint information from EPM/COOP; fabric cardinformation from Spines 102; routing information base (RIB) tables fromnodes in Fabric 120; forwarding information base (FIB) tables from nodesin Fabric 120; security group hardware tables (e.g., TCAM tables) fromnodes in Fabric 120; etc.

In some cases, Unified Collector 314 can obtain runtime state from thenetwork and incorporate runtime state information into L_Model 270Aand/or LR_Model 270B. Unified Collector 314 can also obtain multiplelogical models from Controllers 116 and generate a comprehensive ornetwork-wide logical model (e.g., L_Model 270A and/or LR_Model 270B)based on the logical models. Unified Collector 314 can compare logicalmodels from Controllers 116, resolve dependencies, remove redundancies,etc., and generate a single L_Model 270A and/or LR_Model 270B for theentire network or fabric.

Unified Collector 314 can collect the entire network state acrossControllers 116 and fabric nodes or members (e.g., Leafs 104 and/orSpines 102). For example, Unified Collector 314 can use a REST interfaceand an SSH interface to collect the network state. This informationcollected by Unified Collector 314 can include data relating to the linklayer, VLANs, BDs, VRFs, security policies, etc. The state informationcan be represented in LR_Model 270B, as previously mentioned. UnifiedCollector 314 can then publish the collected information and models toany downstream operators that are interested in or require suchinformation. Unified Collector 314 can publish information as it isreceived, such that data is streamed to the downstream operators.

Data collected by Unified Collector 314 can be compressed and sent todownstream services. In some examples, Unified Collector 314 can collectdata in an online fashion or real-time fashion, and send the datadownstream, as it is collected, for further analysis. In some examples,Unified Collector 314 can collect data in an offline fashion, andcompile the data for later analysis or transmission.

Assurance Appliance 300 can contact Controllers 116, Spines 102, Leafs104, and other nodes to collect various types of data. In somescenarios, Assurance Appliance 300 may experience a failure (e.g.,connectivity problem, hardware or software error, etc.) that prevents itfrom being able to collect data for a period of time. AssuranceAppliance 300 can handle such failures seamlessly, and generate eventsbased on such failures.

Switch Logical Policy Generator 316 can receive L_Model 270A and/orLR_Model 270B from Unified Collector 314 and calculate Li_Model 272 foreach network device i (e.g., switch i) in Fabric 120. For example,Switch Logical Policy Generator 316 can receive L_Model 270A and/orLR_Model 270B and generate Li_Model 272 by projecting a logical modelfor each individual node i (e.g., Spines 102 and/or Leafs 104) in Fabric120. Switch Logical Policy Generator 316 can generate Li_Model 272 foreach switch in Fabric 120, thus creating a switch logical model based onL_Model 270A and/or LR_Model 270B for each switch.

Each Li_Model 272 can represent L_Model 270A and/or LR_Model 270B asprojected or applied at the respective network device i (e.g., switch i)in Fabric 120. In some cases, Li_Model 272 can be normalized orformatted in a manner that is compatible with the respective networkdevice. For example, Li_Model 272 can be formatted in a manner that canbe read or executed by the respective network device. To illustrate,Li_Model 272 can included specific identifiers (e.g., hardware planeidentifiers used by Controllers 116 as classifiers, etc.) or tags (e.g.,policy group tags) that can be interpreted by the respective networkdevice. In some cases, Li_Model 272 can include JSON objects. Forexample, Li_Model 272 can include JSON objects to represent rules,filters, entries, scopes, etc.

The format used for Li_Model 272 can be the same as, or consistent with,the format of Ci_Model 274. For example, both Li_Model 272 and Ci_Model274 may be based on JSON objects. Similar or matching formats can enableLi_Model 272 and Ci_Model 274 to be compared for equivalence orcongruence. Such equivalency checks can aid in network analysis andassurance as further explained herein.

Switch Logical Policy Generator 316 can also perform change analysis andgenerate lint events or records for problems discovered in L_Model 270Aand/or LR_Model 270B. The lint events or records can be used to generatealerts for a user or network operator via an event generator coupled toreceive lint events from Switch Logical Policy Generator 316.

Policy Operator 318 can receive Ci_Model 274 and Hi_Model 276 for eachswitch from Unified Collector 314, and Li_Model 272 for each switch fromSwitch Logical Policy Generator 316, and perform assurance checks andanalysis (e.g., security adherence checks, TCAM utilization analysis,etc.) based on Ci_Model 274, Hi_Model 276, and Li_Model 272. PolicyOperator 318 can perform assurance checks on a switch-by-switch basis bycomparing one or more of the models. The output of Policy Operator 318can be passed to an event generator (not shown) that can generatewarning events for external consumption, where the events correspond tosecurity violations or utilization statistics (such as TCAM usage) thatcomprise a policy violation. Such events are triggered by an abnormal orundesirable network occurrence as determined by the network generator,whereas a notification event might be triggered during the normal courseof performing utilization analysis and security adherence checks in theabsence of any violations.

Returning to Unified Collector 314, Unified Collector 314 can also sendL_Model 270A and/or LR_Model 270B to Routing Policy Parser 320 (for Lmodels), and Ci_Model 274 and Hi_Model 276 to Routing Parser 326 (for Cand H models). Routing Policy Parser 320 can receive L_Model 270A and/orLR_Model 270B and parse the model(s) for information that may berelevant to downstream operators, such as Endpoint Checker 322 andTenant Routing Checker 324. Similarly, Routing Parser 326 can receiveCi_Model 274 and Hi_Model 276 and parse each model for information fordownstream operators, Endpoint Checker 322 and Tenant Routing Checker324.

After Ci_Model 274, Hi_Model 276, L_Model 270A and/or LR_Model 270B areparsed, Routing Policy Parser 320 and/or Routing Parser 326 can sendcleaned-up protocol buffers (Proto Buffs) to the downstream operatorsEndpoint Checker 322 and Tenant Routing Checker 324. Endpoint Checker322 can communicate information related to Endpoint violations, such asduplicate IPs, APIPA, etc. to an event generator capable of generatingevents for external consumption or monitoring. Similarly, Tenant RoutingChecker 324 can communicate information related to the deployment ofBDs, VRFs, subnets, routing table prefixes, etc. to the same ordifferent event generator capable of generating events for externalconsumption or monitoring.

FIG. 3C illustrates a schematic diagram of an example system for staticpolicy analysis in a network (e.g., Network Environment 100). StaticPolicy Analyzer 360 can perform assurance checks to detect configurationviolations, logical lint events, contradictory or conflicting policies,unused contracts, incomplete configurations, etc. Static Policy Analyzer360 can check the specification of the user's intent or intents inL_Model 270A to determine if any configurations in Controllers 116 areinconsistent with the specification of the user's intent or intents.

Static Policy Analyzer 360 can include one or more of the Operators 310executed or hosted in Assurance Appliance 300. However, in otherconfigurations, Static Policy Analyzer 360 can run one or more operatorsor engines that are separate from Operators 310 and/or AssuranceAppliance 300. For example, Static Policy Analyzer 360 can be a VM, acluster of VMs, or a collection of endpoints in a service functionchain.

Static Policy Analyzer 360 can receive as input L_Model 270A fromLogical Model Collection Process 366 and Rules 368 defined for eachfeature (e.g., object) in L_Model 270A. Rules 368 can be based onobjects, relationships, definitions, configurations, and any otherfeatures in MIM 200. Rules 368 can specify conditions, relationships,parameters, and/or any other information for identifying configurationviolations or issues.

Moreover, Rules 368 can include information for identifying syntacticviolations or issues. For example, Rules 368 can include one or morerules for performing syntactic checks. Syntactic checks can verify thatthe configuration of L_Model 270A is complete, and can help identifyconfigurations or rules that are not being used. Syntactic checks canalso verify that the configurations in the hierarchical MIM 200 arecomplete (have been defined) and identify any configurations that aredefined but not used. To illustrate, Rules 368 can specify that everytenant in L_Model 270A should have a context configured; every contractin L_Model 270A should specify a provider EPG and a consumer EPG; everycontract in L_Model 270A should specify a subject, filter, and/or port;etc.

Rules 368 can also include rules for performing semantic checks andidentifying semantic violations or issues. Semantic checks can checkconflicting rules or configurations. For example, Rule1 and Rule2 canhave shadowing issues, Rule1 can be more specific than Rule2 and therebycreate conflicts/issues, etc. Rules 368 can define conditions which mayresult in shadowed rules, conflicting rules, etc. To illustrate, Rules368 can specify that a permit policy for a specific communicationbetween two objects can conflict with a deny policy for the samecommunication between two objects if the permit policy has a higherpriority than the deny policy, or a rule for an object renders anotherrule unnecessary.

Static Policy Analyzer 360 can apply Rules 368 to L_Model 270A to checkconfigurations in L_Model 270A and output Configuration Violation Events370 (e.g., alerts, logs, notifications, etc.) based on any issuesdetected. Configuration Violation Events 370 can include semantic orsemantic problems, such as incomplete configurations, conflictingconfigurations, aliased/shadowed rules, unused configurations, errors,policy violations, misconfigured objects, incomplete configurations,incorrect contract scopes, improper object relationships, etc.

In some cases, Static Policy Analyzer 360 can iteratively traverse eachnode in a tree generated based on L_Model 270A and/or MIM 200, and applyRules 368 at each node in the tree to determine if any nodes yield aviolation (e.g., incomplete configuration, improper configuration,unused configuration, etc.). Static Policy Analyzer 360 can outputConfiguration Violation Events 370 when it detects any violations.

FIG. 4 illustrates an example flowchart for a network assurance model.At step 400, the method involves data collection. Data collection caninclude collection of data for operator intent, such as fabric data(e.g., topology, switch, interface policies, application policies,endpoint groups, etc.), network policies (e.g., BDs, VRFs, L2Outs,L3Outs, protocol configurations, etc.), security policies (e.g.,contracts, filters, etc.), service chaining policies, and so forth. Datacollection can also include data for the concrete, hardware model, suchas network configuration (e.g., RIB/FIB, VLAN, MAC, ISIS, DB, BGP, OSPF,ARP, VPC, LLDP, MTU, QoS, etc.), security policies (e.g., TCAM, ECMPtables, etc.), endpoint dynamics (e.g., EPM, COOP EP DB, etc.),statistics (e.g., TCAM rule hits, interface counters, bandwidth, etc.).

At step 402, the method can involve formal modeling and analysis. Formalmodeling and analysis can involve determining equivalency betweenlogical and hardware models, such as security policies between models,etc.

At step 404, the method can involve smart event generation. Smart eventscan be generated using deep object hierarchy for detailed analysis, suchas: Tenant, Leaf, VRFs, Rules; Filters, Routes, Prefixes, Port Numbers.

At step 406, the method can involve visualization. Formal models can beused to identify problems for analysis and debugging, in a user-friendlyGUI.

Each of the previously described models (Li, Ci, and Hi) is, in one wayor another, derived from the initial L-model that is configured by auser or network operator at the APIC or network controllers. Forexample, the Li model is a logical projection of the fabric-wide L modelonto each leaf, spine, switch, node, etc. i in the network fabric; theCi model is a concrete rendering of the L-model into a format that iscompatible with the aforementioned fabric elements; and the Hi model isthe hardware representation of the Ci model, as stored into switchmemory by a switch memory controller.

Accordingly, each transformation used to derive the Li, Ci, and Himodels presents an opportunity for error or misconfiguration, which isundesirable from a network operator's point of view. These errors canresult due to software bugs, user error, hardware variance, memoryerrors, overflow issues, and other causes that would be appreciated byone of ordinary skill in the art. In some embodiments, each model mayconsist of thousands, or tens of thousands, of distinct rules thatcollectively represent the intents configured in the original L-model,and each of these distinct rules must be preserved when convertingbetween the different models.

Previous approaches to validating the Li, Ci, and Hi models relied uponbrute force, treating each model as a black box and simply comparing theoutputs from the models when given the same input. Such an approach canindicate a lack of equivalence if two models produce a different outputfor the same given input, but cannot provide any insight as to why thereis a conflict. In particular, the black box approach is unable toprovide specific information regarding the specific rules in each modelthat are in conflict, or specific information regarding the specificcontract or intent configured by a user or network operator thatultimately led to the conflict arising.

Rather than employing brute force to determine the equivalence of twoinput models of network intents, the network intent models can insteadbe represented as Reduced Ordered Binary Decision Diagrams (ROBDDs),where each ROBDD is canonical (unique) to the input rules and theirpriority ordering. Each network intent model is first converted to aflat list of priority ordered rules (e.g. contracts defined by acustomer or network operator are defined between EPGs, and rules are thespecific node-to-node implementation of these contracts). In the examplearchitecture 500 depicted in FIG. 5 , flat lists of priority orderedrules for a plurality of models of network intents 506 (illustrated hereare the models Li, Hi, and Ci) are received as input at an ROBDDgenerator 530 of a formal analysis engine 502. These rules can berepresented as Boolean functions, where each rule consists of an action(e.g. Permit, Permit-Log, Deny, Deny-Log) and a set of conditions thatwill trigger that action (e.g. various configurations of packet source,destination, port, header, etc.). For example, a simple rule might bedesigned as Permit all traffic on port 80. In some embodiments, eachrule might be a 147 bit string, with 13 fields of key-value pairs.

As illustrated, ROBDD generator 530 generates an ROBDD for each of theinput models 506, i.e. L_(BDD) for Li, H_(BDD) for Hi, and C_(BDD) forCi. From these ROBDDs, the formal equivalence of any two or more ROBDDsof network intent models can be checked via Equivalence Checker 540,which builds a conflict ROBDD 542 encoding the areas of conflict betweeninput ROBDDs, such as the illustrated input pairs (L_(BDD), H_(BDD)),(L_(BDD), C_(BDD)), and (H_(BDD), C_(BDD)). In particular, the conflictROBDD encodes the areas of conflict between the input ROBDDs. Forexample equivalence checker 540 can check the formal equivalence ofL_(BDD) against H_(BDD) by calculating the exclusive disjunction betweenL_(BDD) and H_(BDD), given as L_(BDD)⊕H_(BDD) (i.e. L_(BDD) XORH_(BDD)).

Equivalence checker 540 then transmits the conflict ROBDD 542 to aconflict rules identifier 550. As illustrated, conflict rules identifier550 can additionally receive the underlying models of network intents506 (e.g. Li, Hi, and Ci) that are received as inputs to formal analysisengine 502. Continuing the previous example wherein a conflict ROBDD wascalculated for L_(BDD) and H_(BDD), specific conflict rules areidentified by iterating over H_(BDD) and calculating the intersectionbetween each individual constituent rule of H_(BDD) and the conflictROBDD L_(BDD) H_(BDD). If the intersection is non-zero, then the givenrule of H_(BDD) is a conflict rule. A similar process can be performedfor each individual constituent rule of L_(BDD), thereby forming alisting of conflict rules for each model (out of the plurality of models506) represented in the particular conflict ROBDD 542. It is alsopossible that conflict rules identifier 550 treats one of the models ofnetwork intent as a reference standard, where the model is assumed to becorrect. As such, conflict rules identifier 550 only needs to computethe intersection of the conflict ROBDD 542 and the set of rules from thenon-reference model. For example, the Li model of user intents can betreated as a reference or standard, because it is directly derived fromuser inputs, received for example at a network controller or APIC. TheHi model, on the other hand, passes through several transformationsbefore ultimately being rendered into a hardware form by a switch memorycontroller, and is therefore much more likely to be subject to error.Accordingly, the conflict rules identifier 550 might only compute(L_(BDD)⊕H_(BDD))*H_(j) for each of the rules j in the Hi model, whichcan cut the required computation time roughly in half.

Regardless of whether conflict rules are identified for both inputmodels of network intent, or only a non-reference model of networkintent, conflict rules identifier 550 then outputs the calculatedconflict rules 552 to an event generator 570. As illustrated, eventgenerator 570 can also receive a copy of the conflict ROBDD 542 fromequivalence checker 540 that corresponds to the same models of networkintents encoded within the calculated conflict rules 552. From these twoinputs, event generator 570 is operable to generate various events 572for external consumption. Various events that may be generated by eventgenerator 570 in response to inputs from formal analysis engine 502 arelisted below, although the listing is not presented as limiting. In thisexample, the listed events correspond to two primary equivalencechecking scenarios. The first is the comparison of the Li model to theCi model (L_(BDD) vs. C_(BDD), as illustrated as input to equivalencechecker 540), which verifies whether or not an intent encoded in thelogical Li model was correctly pushed to switch software as representedin the Ci model. The second is the comparison of the Li model to the Himodel (L_(BDD) vs. H_(BDD), as illustrated as input to equivalencechecker 540), which verifies whether or not an intent encoded in thelogical Li model was correctly rendered into switch hardware asrepresented in the Hi model.

PERMIT_TRAFFIC_MAY_BE_BLOCKED. This event is raised when a contract witha subject, filter, or filter_entry that has an action PERMIT is notfully enforced correctly. The incorrect enforcement could potentiallyarise due to a stale rule that is conflicting in a switch TCAM (i.e. Liconflicts with the Hi model), or if a rule was not pushed properly intothe switch software (Li conflicts with the Ci model).

DENY_TRAFFIC_MAY_BE_PERMITTED. This event is raised when a contract witha subject, filter, or filter_entry that has an action DENY is not fullyenforced correctly. The incorrect enforcement could potentially arisedue to a stale rule that is conflicting in a switch TCAM (i.e. Liconflicts with the Hi model), or if a rule was not pushed properly intothe switch software (Li conflicts with the Ci model).

PERMIT_LOG_ACTION_VIOLATION. This event is raised when a contract with asubject, filter, or filter_entry has an action PERMIT, LOG that is notfully enforced correctly. The incorrect enforcement could potentiallyarise due to a stale rule that is conflicting in a switch TCAM (i.e. Liconflicts with the Hi model), or if a rule was not pushed properly intothe switch software (Li conflicts with the Ci model).

DENY_LOG_ACTION_VIOLATION. This event is raised when a contract with asubject, filter, or filter_entry has an action DENY, LOG that is notfully enforced correctly. The incorrect enforcement could potentiallyarise due to a stale rule that is conflicting in a switch TCAM (i.e. Liconflicts with the Hi model), or if a rule was not pushed properly intothe switch software (Li conflicts with the Ci model).

ENFORCED_VRF_POLICY_VIOLATION. This event is raised when a given VRF isin the ENFORCED state, but the default rules that were supposed to beprogrammed correctly are discovered to actually contain a violation.This incorrect rule programming could result from a stale rule that isconflicting in a switch TCAM (i.e. Li conflicts with the Hi model), orif a rule was not pushed properly into the switch software (Li conflictswith the Ci model).

UNENFORCED_VRF_POLICY_VIOLATION. This event is raised when a given VRFis in the UNENFORCED state, but the default rules that were supposed tobe programmed correctly are discovered to actually contain a violation.This incorrect rule programming could result from a stale rule that isconflicting in a switch TCAM (i.e. Li conflicts with the Hi model), orif a rule was not pushed properly into the switch software (Li conflictswith the Ci model).

*_ENFORCED. For each of the above events, there is also an associatedpositive event that signals that the contract is correctly enforced inthe TCAM, i.e. Li and Hi agree (there may also be an associated positiveevent that signals that the contract is correctly pushed into switchsoftware, i.e. Li and Ci agree). For example,PERMIT_TRAFFIC_POLICY_ENFORCED signals that a contract with a PERMITaction rule is correctly represented.

It is appreciated that additional events 572 corresponding to networkassurance may also be generated by event generator 570 and that theabove listing is providing for exemplary purposes and is not exhaustive.Furthermore, it is appreciated that the generated events canadditionally comprise a suggested fix, wherein event generator 570 drawsupon the available comprehensive characterization of network state andthe constituent models of network intents 506 underlying the network inorder to determine one or more suggested fixes that will resolvewhatever conflict triggered the event.

For each event 572, event generator 570 can adjust the level ofgranularity at which the event is investigated or triggered in order toavoid flooding an excessive number of events downstream. For example,events can be generated at the Provider EPG, Consumer EPG, Contract,Subject, Filter, and FilterEntry levels, with the above listingpresented in order of increasing granularity (i.e. FilterEntry is thefinest level). Additionally, event generator 570 may also aggregateevents, based on factors such as Provider EPG or Consumer EPG, in orderto further reduce event flooding.

Although not shown, formal analysis engine 502 can additionally includea semantic analysis engine, which locates shadowed and shadowing ruleswithin a single model. For example, a semantic analysis engine mightdetermine that a rule L3 is completely shadowed (made redundant) by thecombination of two higher priority rules L1 and L2. Event generator 570can receive the output from a semantic analysis engine, for example, inthe form of a listing of all rules within a given model of networkintents that are shadowed or act to shadow other rules. A listing ofvarious shadowing events generated by event generator 570 is presentedbelow, although the listing is provided as exemplary rather thanlimiting.

PERMIT_OVERSPECIFICATION. This event is generated when a higher priorityPERMIT fully or partially overlaps with a lower priority PERMIT rule,making at least a portion of the lower priority rule redundant andpossibly a subject to removal.

PERMIT_CONTRADICTION. This event is generated when a higher priorityDENY fully or partially overlaps with a lower priority PERMIT rule,making at least a portion of the lower priority rule unused, andpossibly subject to removal or further inspection to resolve thecontradiction.

DENY_OVERSPECIFICATION. This event is generated when a higher priorityDENY fully or partially overlaps with a lower priority DENY rule, makingat least a portion of the lower priority rule redundant and possiblysubject to removal.

DENY_CONTRADICTION. This event is generated when a higher priorityPERMIT fully or partially overlaps with a lower priority DENY rule,making at least a portion of the lower priority rule unused, andpossibly subject to removal or further inspection to resolve thecontradiction.

The example events described above are directed towards comparisons ofone model to another, or comparisons of the rules within a model. Insome embodiments, event generator 570 may be tasked with generatingevents that are directed towards comparisons of comparisons, e.g. acomparison of the outcome of an Li v. Hi comparison and the outcome ofan Li v. Ci comparison. Turning now to FIG. 5B, an example chart 580 ispresented presenting a broad characterization of categories of suchcomparisons of comparisons. A first column 582 indicates the outcome ofan Li v. Hi comparison—a check mark indicates that the Li and Hi modelsconvey the same network intent, while an x indicates that the Li and Himodels fail to convey the same network intent. Likewise, a second column584 indicates the outcome of an Li v. Ci comparison in the same fashion.Each paired outcome represented by columns 582 and 584 is associatedwith an event 586, such that chart 580 presents an event for eachcategory of outcome among the presented pairings of the Li, Hi, and Cimodels.

The first event 580 a occurs when the Li v. Hi comparison 582 isaffirmative, meaning that the Li model is correctly implemented intoswitch hardware or TCAM, and the Li v. Ci comparison 584 is alsoaffirmative, meaning that the Li model was correctly pushed into switchsoftware. The generated event 580 a indicates “All clear”, as no furtheraction is necessary here as the network is functioning correctly.

The second event 580 b occurs when the Li v. Hi comparison 582 isaffirmative, meaning that the Li model is correctly implemented intoswitch hardware or TCAM, but the Li v. Ci comparison 584 is negative,meaning that the Li model was not correctly pushed into switch software.This second event 580 b might trigger a warning or notification thatinforms a user or network operator of the mismatch. However, thiswarning is not immediately severe, as this specific event does notimpact traffic—traffic ultimately is affected only by the hardware modelof the network, and the Li v. Hi comparison 582 was affirmative. In sucha scenario, while a user or network operator will likely be interestedin resolving whatever issue caused the equivalence failure in renderingLi into switch hardware Ci, there is not necessarily any immediatevisible impact on the network. In some instances, the scenario couldsimply be monitored more closely, to see if the Li v. Hi comparison 582also fails at some point. In general, this second event 580 b meritsinvestigation, as it is somewhat unusual to see the Li v. Hi comparison582 pass when the Li v. Ci comparison 584 fails. In general, if Li v. Cifails, it is more often expected that Li v Hi will also then fail,unless an error is present which inadvertently resolves the conflictfound in Li v. Ci.

The third event 580 c occurs when the Li v. Hi comparison 582 isnegative, meaning that the Li model is incorrectly implemented in switchhardware or TCAM, but the Li v. Ci comparison 584 is affirmative,meaning that the Li model was correctly pushed into switch software.This third event 580 b might trigger an error or urgent conflictwarning, as this specific event is traffic impacting—the switches andthe network fabric are not handling traffic in a manner that isequivalent to the intents specified by a user or network operator viathe Li model. The response to such an event might be to reboot theswitch or node i corresponding to the Li, Hi, and Ci models, orotherwise cause the switch TCAM or memory to be flushed. The end resultis that the switch memory is purged, and a fresh copy of Hi is rendered.This third event most often occurs when stale rules linger in TCAM,causing rules from to fail to make it into Hi once TCAM or switch memoryhas reached its full storage capacity.

The fourth event 580 d occurs when the Li v. Hi comparison 582 isnegative, meaning that the Li model is incorrectly implemented in switchhardware or TCAM, and the Li v. Ci comparison 584 is also negative,meaning that the Li model was correctly pushed into switch software.This fourth event 580 d might trigger an automatic new push to theswitch i corresponding to the Li, Hi, and Ci models, wherein an APIC ornetwork controller pushes a copy of Li to the switch or otherwise causesthe switch to freshly determine Ci. This push might be automatic becauseit is most often expected that a failure in the Li v. Ci equivalencecheck 584 will also result in a failure in the Li v. Hi equivalencecheck 582. If this new push to the switch does not resolve the issue,then further investigation and intervention may be needed.

The disclosure now turns to FIGS. 6 and 7 , which illustrate examplenetwork devices and computing devices, such as switches, routers, loadbalancers, client devices, and so forth.

FIG. 6 illustrates an example network device 600 suitable for performingswitching, routing, load balancing, and other networking operations.Network device 600 includes a central processing unit (CPU) 604,interfaces 602, and a bus 610 (e.g., a PCI bus). When acting under thecontrol of appropriate software or firmware, the CPU 604 is responsiblefor executing packet management, error detection, and/or routingfunctions. The CPU 604 preferably accomplishes all these functions underthe control of software including an operating system and anyappropriate applications software. CPU 604 may include one or moreprocessors 608, such as a processor from the INTEL x86 family ofmicroprocessors. In some cases, processor 608 can be specially designedhardware for controlling the operations of network device 600. In somecases, a memory 606 (e.g., non-volatile RAM, ROM, etc.) also forms partof CPU 604. However, there are many different ways in which memory couldbe coupled to the system.

The interfaces 602 are typically provided as modular interface cards(sometimes referred to as “line cards”). Generally, they control thesending and receiving of data packets over the network and sometimessupport other peripherals used with the network device 600. Among theinterfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces, andthe like. In addition, various very high-speed interfaces may beprovided such as fast token ring interfaces, wireless interfaces,Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces, HSSIinterfaces, POS interfaces, FDDI interfaces, WiFi interfaces, 3G/4G/5Gcellular interfaces, CAN BUS, LoRA, and the like. Generally, theseinterfaces may include ports appropriate for communication with theappropriate media. In some cases, they may also include an independentprocessor and, in some instances, volatile RAM. The independentprocessors may control such communications intensive tasks as packetswitching, media control, signal processing, crypto processing, andmanagement. By providing separate processors for the communicationsintensive tasks, these interfaces allow the master microprocessor 604 toefficiently perform routing computations, network diagnostics, securityfunctions, etc.

Although the system shown in FIG. 6 is one specific network device ofthe present invention, it is by no means the only network devicearchitecture on which the present invention can be implemented. Forexample, an architecture having a single processor that handlescommunications as well as routing computations, etc., is often used.Further, other types of interfaces and media could also be used with thenetwork device 600.

Regardless of the network device's configuration, it may employ one ormore memories or memory modules (including memory 606) configured tostore program instructions for the general-purpose network operationsand mechanisms for roaming, route optimization and routing functionsdescribed herein. The program instructions may control the operation ofan operating system and/or one or more applications, for example. Thememory or memories may also be configured to store tables such asmobility binding, registration, and association tables, etc. Memory 606could also hold various software containers and virtualized executionenvironments and data.

The network device 600 can also include an application-specificintegrated circuit (ASIC), which can be configured to perform routingand/or switching operations. The ASIC can communicate with othercomponents in the network device 600 via the bus 610, to exchange dataand signals and coordinate various types of operations by the networkdevice 600, such as routing, switching, and/or data storage operations,for example.

FIG. 7 illustrates a computing system architecture 700 wherein thecomponents of the system are in electrical communication with each otherusing a connection 705, such as a bus. Exemplary system 700 includes aprocessing unit (CPU or processor) 710 and a system connection 705 thatcouples various system components including the system memory 715, suchas read only memory (ROM) 720 and random access memory (RAM) 725, to theprocessor 710. The system 700 can include a cache of high-speed memoryconnected directly with, in close proximity to, or integrated as part ofthe processor 710. The system 700 can copy data from the memory 715and/or the storage device 730 to the cache 712 for quick access by theprocessor 710. In this way, the cache can provide a performance boostthat avoids processor 710 delays while waiting for data. These and othermodules can control or be configured to control the processor 710 toperform various actions. Other system memory 715 may be available foruse as well. The memory 715 can include multiple different types ofmemory with different performance characteristics. The processor 710 caninclude any general purpose processor and a hardware or softwareservice, such as service 1 732, service 2 734, and service 3 736 storedin storage device 730, configured to control the processor 710 as wellas a special-purpose processor where software instructions areincorporated into the actual processor design. The processor 710 may bea completely self-contained computing system, containing multiple coresor processors, a bus, memory controller, cache, etc. A multi-coreprocessor may be symmetric or asymmetric.

To enable user interaction with the computing device 700, an inputdevice 745 can represent any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. An outputdevice 735 can also be one or more of a number of output mechanismsknown to those of skill in the art. In some instances, multimodalsystems can enable a user to provide multiple types of input tocommunicate with the computing device 700. The communications interface740 can generally govern and manage the user input and system output.There is no restriction on operating on any particular hardwarearrangement and therefore the basic features here may easily besubstituted for improved hardware or firmware arrangements as they aredeveloped.

Storage device 730 is a non-volatile memory and can be a hard disk orother types of computer readable media which can store data that areaccessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMs) 725, read only memory (ROM) 720, andhybrids thereof.

The storage device 730 can include services 732, 734, 736 forcontrolling the processor 710. Other hardware or software modules arecontemplated. The storage device 730 can be connected to the systemconnection 705. In one aspect, a hardware module that performs aparticular function can include the software component stored in acomputer-readable medium in connection with the necessary hardwarecomponents, such as the processor 710, connection 705, output device735, and so forth, to carry out the function.

For clarity of explanation, in some instances the present technology maybe presented as including individual functional blocks includingfunctional blocks comprising devices, device components, steps orroutines in a method embodied in software, or combinations of hardwareand software.

In some embodiments the computer-readable storage devices, mediums, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implementedusing computer-executable instructions that are stored or otherwiseavailable from computer readable media. Such instructions can comprise,for example, instructions and data which cause or otherwise configure ageneral purpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The computer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, flash memory, USB devices provided with non-volatile memory,networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprisehardware, firmware and/or software, and can take any of a variety ofform factors. Typical examples of such form factors include laptops,smart phones, small form factor personal computers, personal digitalassistants, rackmount devices, standalone devices, and so on.Functionality described herein also can be embodied in peripherals oradd-in cards. Such functionality can also be implemented on a circuitboard among different chips or different processes executing in a singledevice, by way of further example.

The instructions, media for conveying such instructions, computingresources for executing them, and other structures for supporting suchcomputing resources are means for providing the functions described inthese disclosures.

Although a variety of examples and other information was used to explainaspects within the scope of the appended claims, no limitation of theclaims should be implied based on particular features or arrangements insuch examples, as one of ordinary skill would be able to use theseexamples to derive a wide variety of implementations. Further andalthough some subject matter may have been described in languagespecific to examples of structural features and/or method steps, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features or acts. Forexample, such functionality can be distributed differently or performedin components other than those identified herein. Rather, the describedfeatures and steps are disclosed as examples of components of systemsand methods within the scope of the appended claims.

Claim language reciting “at least one of” refers to at least one of aset and indicates that one member of the set or multiple members of theset satisfy the claim. For example, claim language reciting “at leastone of A and B” means A, B, or A and B.

What is claimed is:
 1. A method comprising: receiving first and secondmodels of network intents, the second model being derived from the firstmodel, each of the first and second models having model rules;representing each of the first and second models using a representationunique to the model rules of each model; identifying whether anequivalence conflict exists between the representations of the first andsecond models; generating, in response to a positive result of theidentifying, a conflict representation that encodes areas of conflictbetween the unique representations of the first and second models;calculating an intersection between each of at least one individualconstituent rule of the representation of the second model and theconflict representation, the intersection reflecting an equivalencefailure between the first and second models; identifying one or moreconstituent intents that caused the equivalence failure; determining thegranularity of the equivalence failure and the identified one or moreconstituent intents; and generating an event for external consumption,the event based at least in part on the equivalence failure, thegranularity of the equivalence failure, and the identified one or moreconstituent intents.
 2. The method of claim 1, wherein therepresentation of the first model, the representation of the secondmodel, and the conflict representation are Reduced Ordered BinaryDecision Diagrams (ROBDDs).
 3. The method of claim 2, wherein thecalculating an intersection comprises calculating an exclusivedisjunction between the conflict ROBDD and each of at least oneindividual constituent rule of the ROBDD of the first model.
 4. Themethod of claim 1, wherein the representing comprises converting thefirst and second models to a flat list of priority ordered rules.
 5. Themethod of claim 4, wherein the rules represent specific node-to-nodeimplementation of contracts defined by a customer or network operatorbetween EPGs, and rules are the specific node-to-node implementation ofthese contracts.
 6. The method of claim 1, wherein the identifyingcomprises calculating an exclusive disjunction between the first andsecond models.
 7. The method of claim 1, wherein the first model is areference model that is presumed correct, such that any equivalencefailure reflected by the calculating represents an error in the secondmodel.
 8. A non-transitory computer readable medium storing instructionsfor execution by a processor, which when executed by the processor causethe processor to perform operations comprising: receive first and secondmodels of network intents, the second model being derived from the firstmodel, each of the first and second models having model rules; representeach of the first and second models using a representation unique to themodel rules of each model; identify whether an equivalence conflictexists between the representations of the first and second models;generate, in response to a positive result of the identifying, aconflict representation that encodes areas of conflict between therepresentations of the first and second models; calculate anintersection between each of at least one individual constituent rule ofthe representation of the second model and the conflict representation,the intersection reflecting an equivalence failure between the first andsecond models; identify one or more constituent intents that caused theequivalence failure; determine the granularity of the equivalencefailure and the identified one or more constituent intents; and generatean event for external consumption, the event based at least in part onthe equivalence failure, the granularity of the equivalence failure, andthe identified one or more constituent intents.
 9. The non-transitorycomputer readable medium of claim 8, wherein the represent comprisesconvert the first and second models to a flat list of priority orderedrules.
 10. The non-transitory computer readable medium of claim 9,wherein the rules represent specific node-to-node implementation ofcontracts defined by a customer or network operator between EPGs, andrules are the specific node-to-node implementation of these contracts.11. The non-transitory computer readable medium of claim 8, wherein theidentify comprises calculating an exclusive disjunction between thefirst and second models.
 12. The non-transitory computer readable mediumof claim 8, wherein the representation of the first model, therepresentation of the second model, and the conflict representation areReduced Ordered Binary Decision Diagrams (ROBDDs).
 13. Thenon-transitory computer readable medium of claim 12, wherein thecalculate an intersection comprises calculate an exclusive disjunctionbetween the conflict ROBDD and each of at least one individualconstituent rule of the ROBDD of the first model.
 14. The non-transitorycomputer readable medium of claim 8, wherein the first model is areference model that is presumed correct, such that any equivalencefailure reflected by the calculate represents an error in the secondmodel.
 15. A system, comprising: a non-transitory computer readablemedium storing instructions; and a processor programmed to cooperatewith the instructions to cause the system to perform operationscomprising: receive first and second models of network intents, thesecond model being derived from the first model, each of the first andsecond models having model rules; represent each of the first and secondmodels using a representation unique to the model rules of each model;identify whether an equivalence conflict exists between therepresentations of the first and second models; generate, in response toa positive result of the identifying, a conflict representation thatencodes areas of conflict between the representations of the first andsecond models; calculate an intersection between each of at least oneindividual constituent rule of the representation of the second modeland the conflict representation, the intersection reflecting anequivalence failure between the first and second models; identify one ormore constituent intents that caused the equivalence failure; determinethe granularity of the equivalence failure and the identified one ormore constituent intents; and generate an event for externalconsumption, the event based at least in part on the equivalencefailure, the granularity of the equivalence failure, and the identifiedone or more constituent intents.
 16. The system of claim 15, wherein therepresent comprises convert the first and second models to a flat listof priority ordered rules.
 17. The system of claim 16, wherein the rulesrepresent specific node-to-node implementation of contracts defined by acustomer or network operator between EPGs, and rules are the specificnode-to-node implementation of these contracts.
 18. The system of claim15, wherein the identify comprises calculating an exclusive disjunctionbetween the first and second models.
 19. The system of claim 15, whereinthe representation of the first model, the representation of the secondmodel, and the conflict representation are Reduced Ordered BinaryDecision Diagrams (ROBDDs).
 20. The system of claim 19, wherein thecalculate an intersection comprises calculate an exclusive disjunctionbetween the conflict ROBDD and each of at least one individualconstituent rule of the ROBDD of the first model.